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Description
In this Barrett Honors Thesis, I developed a model to quantify the complexity of Sankey diagrams, which are a type of visualization technique that shows flow between groups. To do this, I created a carefully controlled dataset of synthetic Sankey diagrams of varying sizes as study stimuli. Then, a pair of online crowdsourced user studies were conducted and analyzed. User performance for Sankey diagrams of varying size and features (number of groups, number of timesteps, and number of flow crossings) were algorithmically modeled as a formula to quantify the complexity of these diagrams. Model accuracy was measured based on the performance of users in the second crowdsourced study. The results of my experiment conclusively demonstrates that the algorithmic complexity formula I created closely models the visual complexity of the Sankey Diagrams in the dataset.
ContributorsGinjpalli, Shashank (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Oftentimes, patients struggle to accurately describe their symptoms to medical professionals, which produces erroneous diagnoses, delaying and preventing treatment. My app, Augnosis, will streamline constructive communication between patient and doctor, and allow for more accurate diagnoses. The goal of this project was to create an app capable of gathering data on visual symptoms of facial acne and categorizing it to differentiate between diagnoses using image recognition and identification. “Augnosis”, is a combination of the words “Augmented Reality” and “Self-Diagnosis”, the former being the medium in which it is immersed and the latter detailing its functionality.
ContributorsGoyal, Nandika (Author) / Johnson, Mina (Thesis director) / Bryan, Chris (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05
Description
For this creative project, I created a visually immersive and artistic data visualization of global space-related activities. The project aims to create a sense of wonder and creativity for space exploration through unconventional data visualization. By focusing mainly on the artistic elements of the visualization, the project will have a larger emotional impact on its viewers, as opposed to a traditional data visualization. The project uses a comprehensive dataset of space-related articles, all of which include the location of the activity discussed in the article, as well as keywords and other fields. The dataset will serve as material to create a narrative that shows not only how space-related activities are distributed around the globe but also the overarching themes of the activities. To create the final project, I used the JavaScript library p5.js.
ContributorsDeb, Roshni (Author) / Bryan, Chris (Thesis director) / Zhang, Weidi (Committee member) / Barrett, The Honors College (Contributor) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
This thesis serves as an experimental investigation into the potential of machine learning through attempting to predict the future price of a cryptocurrency. Through the use of web scraping, short interval data was collected on both Bitcoin and Dogecoin. Dogecoin was the dataset that was eventually used in this thesis due to its relative stability compared to Bitcoin. At the time of the data collection, Bitcoin became a much more frequent topic in the media and had more significant fluctuations due to it. The data was processed into consistent three separate, consistent timesteps, and used to generate predictive models. The models were able to accurately predict test data given all the preceding test data but were unable to autoregressively predict future data given only the first set of test data points. Ultimately, this project helps illustrate the complexities of extended future price prediction when using simple models like linear regression.
ContributorsMurwin, Andrew (Author) / Bryan, Chris (Thesis director) / Ghayekhloo, Samira (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to select an elected official that accurately represents their interests. Instead, gerrymandering has legislators to choose their voters.
In recent years, the Supreme Court has heard challenges to state legislature-drawn districts, most recently in Allen v. Milligan for Alabama and Moore v. Harper for North Carolina. The highest court of the United States ruled that the two state maps were gerrymandered, and in coming to their decision, the 9 justices relied on a plethora of amicus briefs- one of which included the Markov Chain Monte Carlo method, a computational method used to find gerrymandering.
Because of how widespread gerrymandering has become on both sides of the political aisle, states have moved to create independent redistricting commissions. Qualitative research regarding the efficacy of independent commissions is present, but there is little research using the quantitative computational methods from these SCOTUS cases. As a result, my thesis will use the Markov Chain Monte Carlo method to answer if impartial redistricting commissions (like we have in Arizona) actually preclude unfair redistricting practices.
My completed project is located here:
https://dheetideliwala.github.io/honors-thesis/
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDi Russo, Michelle (Conductor) / Alpizar, Mark (Conductor) / Shaker, Shannon (Conductor) / Gupta, Kamna (Conductor) / ASU Library. Music Library (Publisher)
Created2017-11-29
ContributorsPercussion Jazz Ensemble (Performer) / ASU Library. Music Library (Publisher)
Created2017-11-20
ContributorsSmith, J. B., 1957- (Director) / Mancuso, Simone (Director) / Contemporary Percussion Ensemble (Performer) / ASU Library. Music Library (Contributor)
Created2017-11-19
ContributorsSmith, Aaron (Performer) / Solari, John (Performer) / Hammond, Marinne (Performer) / Shaner, Hayden (Performer) / Kempton, Emily (Performer) / Wills, Grace (Performer) / Rumney, Emily (Performer) / Neff, Megyn (Performer) / DiBarry, Michael (Performer) / ASU Library. Music Library (Publisher)
Created2017-11-17